In [6]:

#Importing h2o automl
import h2o
from h2o.automl import H2OAutoML

#Initialise h2o
h2o.init()

# Import data
f = "/content/drive/My Drive/day.csv"
df = h2o.import_file(f)

# Reponse column
y = "cnt"
#removing the columns instant as it is index, date, count of casual and registered.
x=df.columns
x.remove(y)
x.remove("instant")
x.remove("dteday")
x.remove("casual")
x.remove("registered")

# Split into train & test
splits = df.split_frame(ratios = [0.8], seed = 1)
train = splits[0]
test = splits[1]

Checking whether there is an H2O instance running at http://localhost:54321. connected.


H2O_cluster_uptime:	2 mins 12 secs
H2O_cluster_timezone:	Etc/UTC
H2O_data_parsing_timezone:	UTC
H2O_cluster_version:	3.40.0.2
H2O_cluster_version_age:	5 days
H2O_cluster_name:	H2O_from_python_unknownUser_gb467k
H2O_cluster_total_nodes:	1
H2O_cluster_free_memory:	3.172 Gb
H2O_cluster_total_cores:	2
H2O_cluster_allowed_cores:	2
H2O_cluster_status:	locked, healthy
H2O_connection_url:	http://localhost:54321
H2O_connection_proxy:	{"http": null, "https": null}
H2O_internal_security:	False
Python_version:	3.9.16 final

Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%

In [7]:

# Run AutoML for 1 minute
aml = H2OAutoML(max_runtime_secs=60, seed=1)
aml.train(x=x,y=y, training_frame=train)

# Explain AutoML models
exa = aml.explain(test)

AutoML progress: |███████████████████████████████████████████████████████████████| (done) 100%

Leaderboard

Leaderboard shows models with their metrics. When provided with H2OAutoML object, the leaderboard shows 5-fold cross-validated metrics by default (depending on the H2OAutoML settings), otherwise it shows metrics computed on the frame. At most 20 models are shown by default.

model_id	rmse	mse	mae	rmsle	mean_residual_deviance	training_time_ms	predict_time_per_row_ms	algo
XGBoost_grid_1_AutoML_1_20230315_105904_model_7	681.761	464798	477.944	0.226944	464798	245	0.009718	XGBoost
StackedEnsemble_BestOfFamily_4_AutoML_1_20230315_105904	686.273	470970	463.034	0.222515	470970	147	0.551172	StackedEnsemble
GBM_grid_1_AutoML_1_20230315_105904_model_2	686.694	471549	476.466	0.223194	471549	180	0.049775	GBM
StackedEnsemble_AllModels_3_AutoML_1_20230315_105904	690.826	477241	468.6	0.220552	477241	295	0.387439	StackedEnsemble
GBM_grid_1_AutoML_1_20230315_105904_model_5	697.168	486043	470.803	0.230414	486043	223	0.019523	GBM
GBM_2_AutoML_1_20230315_105904	704.205	495904	481.976	0.233554	495904	296	0.153763	GBM
GBM_grid_1_AutoML_1_20230315_105904_model_3	704.968	496979	493.514	0.226412	496979	174	0.055351	GBM
StackedEnsemble_AllModels_2_AutoML_1_20230315_105904	708.342	501748	475.152	0.226977	501748	289	0.621739	StackedEnsemble
XGBoost_grid_1_AutoML_1_20230315_105904_model_2	708.362	501777	500.027	0.241927	501777	499	0.010676	XGBoost
StackedEnsemble_BestOfFamily_3_AutoML_1_20230315_105904	709.662	503620	474.924	0.228344	503620	256	0.257056	StackedEnsemble
GBM_5_AutoML_1_20230315_105904	712.352	507446	493.318	0.237825	507446	213	0.241597	GBM
GBM_3_AutoML_1_20230315_105904	714.485	510489	497.367	0.23268	510489	241	0.064085	GBM
StackedEnsemble_AllModels_1_AutoML_1_20230315_105904	716.062	512745	481.947	0.229252	512745	315	0.212866	StackedEnsemble
XGBoost_3_AutoML_1_20230315_105904	717.333	514566	499.827	0.245668	514566	454	0.050805	XGBoost
StackedEnsemble_BestOfFamily_2_AutoML_1_20230315_105904	717.931	515424	482.091	0.231303	515424	152	0.125964	StackedEnsemble
GBM_4_AutoML_1_20230315_105904	721.362	520364	489.425	0.235171	520364	294	0.13815	GBM
XGBoost_grid_1_AutoML_1_20230315_105904_model_4	734.601	539639	512.2	0.221314	539639	256	0.125684	XGBoost
DRF_1_AutoML_1_20230315_105904	748.41	560118	517.724	0.257692	560118	493	0.056815	DRF
StackedEnsemble_BestOfFamily_1_AutoML_1_20230315_105904	750.313	562970	512.777	0.228172	562970	346	0.05625	StackedEnsemble
GBM_grid_1_AutoML_1_20230315_105904_model_4	750.602	563403	533.795	0.244205	563403	245	0.173225	GBM

[20 rows x 9 columns]

Residual Analysis

Residual Analysis plots the fitted values vs residuals on a test dataset. Ideally, residuals should be randomly distributed. Patterns in this plot can indicate potential problems with the model selection, e.g., using simpler model than necessary, not accounting for heteroscedasticity, autocorrelation, etc. Note that if you see "striped" lines of residuals, that is an artifact of having an integer valued (vs a real valued) response variable.

Learning Curve Plot

Learning curve plot shows the loss function/metric dependent on number of iterations or trees for tree-based algorithms. This plot can be useful for determining whether the model overfits.

Variable Importance

The variable importance plot shows the relative importance of the most important variables in the model.

Variable Importance Heatmap

Variable importance heatmap shows variable importance across multiple models. Some models in H2O return variable importance for one-hot (binary indicator) encoded versions of categorical columns (e.g. Deep Learning, XGBoost). In order for the variable importance of categorical columns to be compared across all model types we compute a summarization of the the variable importance across all one-hot encoded features and return a single variable importance for the original categorical feature. By default, the models and variables are ordered by their similarity.

Model Correlation

This plot shows the correlation between the predictions of the models. For classification, frequency of identical predictions is used. By default, models are ordered by their similarity (as computed by hierarchical clustering). Interpretable models, such as GAM, GLM, and RuleFit are highlighted using red colored text.

SHAP Summary

SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function.

Partial Dependence Plots

Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.

Individual Conditional Expectation

An Individual Conditional Expectation (ICE) plot gives a graphical depiction of the marginal effect of a variable on the response. ICE plots are similar to partial dependence plots (PDP); PDP shows the average effect of a feature while ICE plot shows the effect for a single instance. This function will plot the effect for each decile. In contrast to the PDP, ICE plots can provide more insight, especially when there is stronger feature interaction.

In [8]:

# As per above AutoML model explained the different models using XGBOOST,GBM and Stacked Ensemble models.
## Varible importance and variable importance heat map shows year has more influence followed temperature,humidity,season on the target variable.

In [9]:

# Explain a single H2O model (e.g. leader model from AutoML)
exm = aml.leader.explain(test)

Residual Analysis

Residual Analysis plots the fitted values vs residuals on a test dataset. Ideally, residuals should be randomly distributed. Patterns in this plot can indicate potential problems with the model selection, e.g., using simpler model than necessary, not accounting for heteroscedasticity, autocorrelation, etc. Note that if you see "striped" lines of residuals, that is an artifact of having an integer valued (vs a real valued) response variable.

Learning Curve Plot

Learning curve plot shows the loss function/metric dependent on number of iterations or trees for tree-based algorithms. This plot can be useful for determining whether the model overfits.

Partial Dependence Plots

Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.

Individual Conditional Expectation

An Individual Conditional Expectation (ICE) plot gives a graphical depiction of the marginal effect of a variable on the response. ICE plots are similar to partial dependence plots (PDP); PDP shows the average effect of a feature while ICE plot shows the effect for a single instance. This function will plot the effect for each decile. In contrast to the PDP, ICE plots can provide more insight, especially when there is stronger feature interaction.

In [8]:

#As per above stacked ensemble model is the leader model

In [11]:

#Using H2OGradientBoostingEstimator
from h2o.estimators.gbm import H2OGradientBoostingEstimator
gbm = H2OGradientBoostingEstimator(distribution="quantile", quantile_alpha=0.8)
gbm.train(x=x,y="cnt", training_frame=train)
gbm.explain(test)

gbm Model Build progress: |██████████████████████████████████████████████████████| (done) 100%

Residual Analysis

Residual Analysis plots the fitted values vs residuals on a test dataset. Ideally, residuals should be randomly distributed. Patterns in this plot can indicate potential problems with the model selection, e.g., using simpler model than necessary, not accounting for heteroscedasticity, autocorrelation, etc. Note that if you see "striped" lines of residuals, that is an artifact of having an integer valued (vs a real valued) response variable.

Learning Curve Plot

Learning curve plot shows the loss function/metric dependent on number of iterations or trees for tree-based algorithms. This plot can be useful for determining whether the model overfits.

Variable Importance

The variable importance plot shows the relative importance of the most important variables in the model.

SHAP Summary

SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function.

Partial Dependence Plots

Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.

Individual Conditional Expectation

An Individual Conditional Expectation (ICE) plot gives a graphical depiction of the marginal effect of a variable on the response. ICE plots are similar to partial dependence plots (PDP); PDP shows the average effect of a feature while ICE plot shows the effect for a single instance. This function will plot the effect for each decile. In contrast to the PDP, ICE plots can provide more insight, especially when there is stronger feature interaction.

Residual Analysis

Residual Analysis plots the fitted values vs residuals on a test dataset. Ideally, residuals should be randomly distributed. Patterns in this plot can indicate potential problems with the model selection, e.g., using simpler model than necessary, not accounting for heteroscedasticity, autocorrelation, etc. Note that if you see "striped" lines of residuals, that is an artifact of having an integer valued (vs a real valued) response variable.

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Learning Curve Plot

Learning curve plot shows the loss function/metric dependent on number of iterations or trees for tree-based algorithms. This plot can be useful for determining whether the model overfits.

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Variable Importance

The variable importance plot shows the relative importance of the most important variables in the model.

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SHAP Summary

SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function.

<h2o.plot._plot_result._MObject at 0x7f17a0035160>

Partial Dependence Plots

Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.

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Individual Conditional Expectation

An Individual Conditional Expectation (ICE) plot gives a graphical depiction of the marginal effect of a variable on the response. ICE plots are similar to partial dependence plots (PDP); PDP shows the average effect of a feature while ICE plot shows the effect for a single instance. This function will plot the effect for each decile. In contrast to the PDP, ICE plots can provide more insight, especially when there is stronger feature interaction.

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